The second paradigm resolves the aforementioned problem by making better use of the latent space of GAN through optimization. However, as the low-dimensional latent codes and the constraints in the image space are insufficient to guide the restoration process, these methods often generate images with low fidelity. As shown in Fig. REF , despite being realistic, the output of a representative method, PULSE [1]}, fails to recover the structures of the ground-truth faithfully.
In addition, as the optimization is usually conducted in an iterative manner for each image at runtime, these approaches are often time-consuming.
